import numpy as np
import cv2
import torch
from torchvision import models, transforms
from hmmlearn import hmm
from scipy import signal
from skimage import feature, color, exposure
import librosa

class PerceptionSpace:
    """Perception Space implementation (Section 3.1)"""
    def __init__(self):
        self.memory = []
        
        # Initialize efficient CNN for visual processing
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.visual_model = models.efficientnet_b0(pretrained=True).to(self.device)
        self.visual_model.eval()
        
        # Visual preprocessing
        self.preprocess = transforms.Compose([
            transforms.ToPILImage(),
            transforms.Resize((224, 224)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                              std=[0.229, 0.224, 0.225])
        ])
        
        # Initialize HMM for audio processing
        self.hmm_model = hmm.GaussianHMM(n_components=5, covariance_type="full")
        
        # Multimodal fusion parameters
        self.fusion_weights = {
            "visual": 0.6,
            "audio": 0.4
        }

    def analyze_visual_signal(self, visual_input):
        """Process visual signals as defined in Section 3.1"""
        with torch.no_grad():
            # Preprocess image
            if isinstance(visual_input, np.ndarray):
                visual_input = self.preprocess(visual_input).unsqueeze(0)
            visual_input = visual_input.to(self.device)
            
            # Extract features using EfficientNet
            features = self.visual_model.features(visual_input)
            
            # Extract traditional visual features
            visual_features = {
                "deep_features": features.cpu().numpy(),
                "spatial_features": self._extract_spatial_features(visual_input),
                "semantic_features": self._extract_semantic_features(visual_input)
            }
            
            return visual_features

    def analyze_audio_signal(self, audio_data):
        """
        Analyze the audio signal and extract features using librosa.
        
        Args:
            audio_data (dict): Audio information containing 'waveform' and 'sr'
            
        Returns:
            dict: A dictionary of extracted audio features.
        """
        try:
            waveform = audio_data["waveform"]
            sr = audio_data["sr"]
            # Extract audio features using librosa
            mfccs = librosa.feature.mfcc(y=waveform, sr=sr, n_mfcc=13)
            chroma = librosa.feature.chroma_stft(y=waveform, sr=sr)
            spectral_contrast = librosa.feature.spectral_contrast(y=waveform, sr=sr)
            tonnetz = librosa.feature.tonnetz(y=waveform, sr=sr)
            
            # Compute the mean value for each feature across frames
            features = {
                "mfccs": mfccs.mean(axis=1).tolist(),
                "chroma": chroma.mean(axis=1).tolist(),
                "spectral_contrast": spectral_contrast.mean(axis=1).tolist(),
                "tonnetz": tonnetz.mean(axis=1).tolist()
            }
            return features
        except Exception as e:
            print(f"Error in analyzing audio signal: {e}")
            return None

    def analyze_audio(self, audio_signal, sample_rate):
        """Audio signal processing using HMM as per paper"""
        # Extract MFCC features
        mfcc_features = self._extract_mfcc(audio_signal, sample_rate)
        
        # Fit HMM model
        self.hmm_model.fit(mfcc_features)
        
        # Get audio characteristics
        audio_score = self.hmm_model.score(mfcc_features)
        
        return {
            "mfcc_features": mfcc_features,
            "hmm_score": audio_score,
            "audio_characteristics": self._analyze_audio_characteristics(audio_signal)
        }

    def _extract_mfcc(self, audio_signal, sample_rate):
        """Extract MFCC features from audio"""
        # Parameters from paper
        frame_length = int(0.025 * sample_rate)  # 25ms frames
        frame_step = int(0.010 * sample_rate)    # 10ms step
        
        # Extract MFCCs
        mfccs = tf.signal.mfccs_from_mel_spectrograms(
            tf.abs(tf.signal.stft(
                audio_signal,
                frame_length=frame_length,
                frame_step=frame_step
            ))
        )
        return mfccs.numpy()

    def _analyze_audio_characteristics(self, audio_signal):
        """Analyze audio signal characteristics"""
        return {
            "energy": np.mean(np.abs(audio_signal)),
            "zero_crossing_rate": np.mean(np.abs(np.diff(np.signbit(audio_signal)))),
            "spectral_centroid": self._calculate_spectral_centroid(audio_signal)
        }

    def multimodal_fusion(self, visual_features, audio_features=None):
        """Multimodal fusion strategies as per Section 3.1"""
        # Early Fusion
        if audio_features is not None:
            # TODO: Implement early fusion strategy
            pass
        
        # Late Fusion
        # TODO: Implement late fusion strategy
        
        # Attention Mechanism
        # TODO: Implement attention mechanism
        
        return visual_features

    def _extract_spatial_features(self, visual_input):
        """Extract spatial features from visual input"""
        # Convert to numpy for OpenCV processing
        img = visual_input.squeeze(0).permute(1,2,0).cpu().numpy()
        
        return {
            "edges": self._analyze_edges(img),
            "texture": self._analyze_texture(img),
            "composition": self._analyze_composition(img)
        }

    def _extract_semantic_features(self, visual_input):
        """Extract semantic features for scene understanding"""
        # Use model's intermediate layers for semantic understanding
        semantic_features = {}
        
        # TODO: Implement semantic feature extraction
        # This could include:
        # - Object detection
        # - Scene classification
        # - Style recognition
        
        return semantic_features

    def _analyze_edges(self, image):
        """Edge analysis"""
        gray = cv2.cvtColor((image * 255).astype(np.uint8), cv2.COLOR_RGB2GRAY)
        edges = cv2.Canny(gray, 100, 200)
        return np.mean(edges) / 255.0

    def _analyze_texture(self, image):
        """Texture analysis"""
        gray = cv2.cvtColor((image * 255).astype(np.uint8), cv2.COLOR_RGB2GRAY)
        glcm = feature.graycomatrix(gray, [1], [0, np.pi/4, np.pi/2, 3*np.pi/4])
        return {
            "contrast": feature.graycoprops(glcm, 'contrast')[0].mean(),
            "homogeneity": feature.graycoprops(glcm, 'homogeneity')[0].mean()
        }

    def _analyze_composition(self, image):
        """Composition analysis"""
        # Rule of thirds
        h, w = image.shape[:2]
        thirds_h = [h//3, 2*h//3]
        thirds_w = [w//3, 2*w//3]
        
        # Analyze intersections
        energy = []
        for i in thirds_h:
            for j in thirds_w:
                region = image[i-5:i+5, j-5:j+5]
                energy.append(np.mean(np.abs(region)))
                
        return np.mean(energy)

    def _analyze_color_harmony(self, image):
        """Analyze color harmony as per Section 3.1.2.a"""
        # Convert to HSV for better color analysis
        hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
        
        # Calculate color histogram
        hist_h = cv2.calcHist([hsv], [0], None, [180], [0, 180])
        hist_s = cv2.calcHist([hsv], [1], None, [256], [0, 256])
        
        # Analyze color distribution
        color_variety = np.std(hist_h) / np.mean(hist_h) if np.mean(hist_h) > 0 else 0
        saturation_balance = np.std(hist_s) / np.mean(hist_s) if np.mean(hist_s) > 0 else 0
        
        return {
            "harmony_score": min(color_variety * self.perception_params["color_sensitivity"], 1.0),
            "color_variety": color_variety,
            "saturation_balance": saturation_balance
        }

    def _calculate_aesthetic_score(self, perception):
        """Calculate overall aesthetic score based on all features"""
        weights = {
            "color": 0.3,
            "composition": 0.3,
            "texture": 0.2,
            "edges": 0.2
        }
        
        score = (
            weights["color"] * perception["color_features"]["harmony_score"] +
            weights["composition"] * perception["composition_features"]["thirds_alignment"] +
            weights["texture"] * perception["texture_features"]["homogeneity"] +
            weights["edges"] * (1 - abs(0.5 - perception["edge_features"]["edge_density"]))
        )
        
        return min(max(score, 0), 1)  # Normalize to [0,1]

    def perceive(self, observation):
        """Process observations and return perception"""
        # TODO: Implement perception processing
        return observation 